DeepSeek Harness: Everything is a Plugin.
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- dsh-plugin
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DeepSeek Harness: Everything is a Plugin.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.
Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
The agent engineering platform.
AI agent toolkit: unified LLM API, agent loop, TUI, coding agent CLI
The 1st Collaborative AI Design Agent Workspace. Local-first and open source. Create motion, prototypes, websites, apps, dashboards, presentations, images, and videos with your team and AI agents. Share work, exchange feedback, and iterate together in one workspace.
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Open-source AI coworkers that each get a computer of their own: a browser, files and tools, with every action decided before it happens and recorded after. Bring any AG-UI agent.
🧩 FrontierAgent, our agent framework, open-sourced alongside it — native command-line TUI, ReAct and Agent Team modes, one command on macOS and Linux, no preinstall, no hard Docker dependency.
Agnes Harness: A pluggable agent harness for Forward Deployed Engineering (FDE). Built for trust. Made for the real world.
RSI-Anything: Unlocking Recursive Self-Improvement with World Models
Build AI agents that run 100% on-device. Sub-100ms latency on Qualcomm NPU. Zero cloud dependency.
Learning to Recover from LLM Agent Failures at Test Time
MCP gateway & OAuth MCP servers. LangGraph agent: memory, eval/tracking, PII guard, context trimming. Docker quick start for enterprise agents.
Build AI phone agents on your own computer and test every change with an AI caller. Open source, runs free with no API keys, cents per call with your own keys. Imports Dialogflow CX.
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A framework for continual learning and recursive self-improvement through harness adapter evolution.
oar (Open Agent Runtime): programming interface for all agent harnesses
A framework for building decision-model harnesses for any kind of task. Your program offers the model a short list of next steps, and the model picks one at a time until it can answer.
Scikit-learn for Recursive Self-improvement (RSI). A modular framework for recursive harness evolution.
A unified framework for building, evaluating, and recursively improving embodied agents. 🚀 Code release coming soon.
Official Code for OpenVLHarness: Real-World Visual Agentic Tool‑Use with Multimodal Memory
An agent that compiles git maintenance chores into reusable programs, then decides whether to replay them by running checks rather than by guessing the task looks familiar
OPAL: a reactive optimizer. A coding agent rewrites the optimizer while your model trains.